The Freedman-Diaconis Rule is a method used in statistics to determine the optimal width of bins for creating histograms. It aims to balance the trade-off between too many bins, which can create noise, and too few bins, which can obscure important data patterns. The rule calculates bin width based on the interquartile range (IQR) of the data and the number of observations. To apply the Freedman-Diaconis Rule, you first find the IQR, which measures the spread of the middle 50% of the data. Then, you divide the IQR by the cube root of the number of data points. This results in a bin width that helps visualize the distribution of the dataset effectively.